5 citations · 9 across the 4 of their papers we have counts for
7 papers
Semi-supervised Learning using Robust Loss
Wenhui Cui, Haleh Akrami, Anand A. Joshi +1
The amount of manually labeled data is limited in medical applications, so semi-supervised learning and automatic labeling strategies can be an asset for training deep neural netwo…
fMRI-Kernel Regression: A Kernel-based Method for Pointwise Statistical Analysis of rs-fMRI for Population Studies
Anand A. Joshi, Soyoung Choi, Haleh Akrami +1
Due to the spontaneous nature of resting-state fMRI (rs-fMRI) signals, cross-subject comparison and therefore, group studies of rs-fMRI are challenging. Most existing group compari…
Realistic head modeling of electromagnetic brain activity: An integrated Brainstorm pipeline from MRI data to the FEM solution
Takfarinas Medani, Juan Garcia-Prieto, Francois Tadel +6
Human brain activity generates scalp potentials (electroencephalography EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography MEG), all capable…
Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression
Haleh Akrami, Anand A. Joshi, Sergul Aydore +1
Estimation of uncertainty in deep learning models is of vital importance, especially in medical imaging, where reliance on inference without taking into account uncertainty could l…
Robust Variational Autoencoder for Tabular Data with Beta Divergence
Haleh Akrami, Sergul Aydore, Richard M. Leahy +1
We propose a robust variational autoencoder with divergence for tabular data (RTVAE) with mixed categorical and continuous features. Variational autoencoders (VAE) and their va…
3D Phase Retrieval at Nano-Scale via Accelerated Wirtinger Flow
Zalan Fabian, Justin Haldar, Richard Leahy +1
Imaging 3D nano-structures at very high resolution is crucial in a variety of scientific fields. However, due to fundamental limitations of light propagation we can only measure th…